Systems and methods for magnetic resonance imaging

ABSTRACT

A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/002,817, filed on Aug. 26, 2020, which claims priority to ChinesePatent Application No. 201910789671.3, filed on Aug. 26, 2019, andChinese Patent Application No. 201911053237.5, filed on Oct. 31, 2019,the contents of each of which are hereby incorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to Magnetic Resonance Imaging (MRI),and more particularly, relates to systems and methods for generating asubsampling model and an MRI reconstruction model.

BACKGROUND

With the development of medical technologies, various medical imagingdevices have emerged to acquire medical images of a subject (e.g., apatient). Among these medical imaging devices, an MRI device, whichcauses little or no ionizing radiation damage to the subject and iscapable of acquiring multi-dimensional information (e.g., T1information, T2 information, etc.) of the subject, has become animportant tool for medical diagnosis and/or treatment.

SUMMARY

An aspect of the present disclosure relates to a system for MagneticResonance Imaging (MRI). The system may include at least one storagedevice including a set of instructions and at least one processor incommunication with the at least one storage device. When executing theset of instructions, the at least one processor may be directed toperform operations. The operations may include obtaining at least onetraining sample each of which includes full MRI data. The operations mayinclude obtaining a preliminary subsampling model and a preliminary MRIreconstruction model. The operations may further include generating asubsampling model corresponding to an MRI reconstruction model byjointly training the preliminary subsampling model and the preliminaryMRI reconstruction model using the at least one training sample. Thesubsampling model may be the trained preliminary subsampling model, andthe MRI reconstruction model may be at least a portion of the trainedpreliminary MRI reconstruction model.

In some embodiments, the jointly training the preliminary subsamplingmodel and the preliminary MRI reconstruction model may include aniterative operation including one or more iterations. The at least oneiteration of the one or more iterations may include obtaining, based onthe preliminary subsampling model and the preliminary MRI reconstructionmodel, an intermediate subsampling model and an intermediate MRIreconstruction model. The at least one iteration of the one or moreiterations may include determining whether the intermediate subsamplingmodel satisfies a termination condition. In response to determining thatthe intermediate subsampling model does not satisfy the terminationcondition, the at least one iteration of the one or more iterations mayfurther include updating, based on the intermediate MRI reconstructionmodel and at least a portion of the at least one training sample, theintermediate subsampling model.

In some embodiments, the at least one iteration may be the firstiteration among the one or more iterations. The intermediate subsamplingmodel may be the preliminary subsampling model and the intermediate MRIreconstruction model may be the preliminary MRI reconstruction model.

In some embodiments, the one or more iterations may include a pluralityof iterations. The at least one iteration may be subsequent to the firstiteration among the one or more iterations. The intermediate subsamplingmodel may be an updated intermediate subsampling model generated in aprevious iteration, and the intermediate MRI reconstruction model may bean updated intermediate subsampling model generated in the previousiteration or the preliminary MRI reconstruction model.

In some embodiments, for the at least one iteration, the determiningwhether the intermediate subsampling model satisfies a terminationcondition may include, for each of the at least a portion of the atleast one training sample, generating a subsampled MRI image based onthe intermediate subsampling model and the full MRI data of the trainingsample. The determining whether the intermediate subsampling modelsatisfies a termination condition may include, for each of the at leasta portion of the at least one training sample, generating a predictedfull MRI image based on the subsampled MRI image and the intermediateMRI reconstruction model. The determining whether the intermediatesubsampling model satisfies a termination condition may also include,for each of the at least a portion of the at least one training sample,generating a determination result of whether the predicted full MRIimage satisfies a preset condition. The determining whether theintermediate subsampling model satisfies a termination condition mayfurther include determining whether the intermediate subsampling modelsatisfies the termination condition based on the determination result ofeach of the at least a portion of the at least one training sample.

In some embodiments, for each of the at least a portion of the at leastone training sample, the generating a determination result of whetherthe predicted full MRI image satisfies a preset condition may includeobtaining a full MRI image based on the full MRI data of the trainingsample. The generating a determination result of whether the predictedfull MRI image satisfies a preset condition may include determining adifference between the full MRI image and the predicted full MRI imageof the training sample. The generating a determination result of whetherthe predicted full MRI image satisfies a preset condition may furtherinclude determining whether the predicted full MRI image satisfies thepreset condition by determining whether the difference exceeds athreshold difference.

In some embodiments, in response to determining that the intermediatesubsampling model does not satisfy the termination condition, theupdating the intermediate subsampling model based on the intermediateMRI reconstruction model and at least a portion of the at least onetraining sample may include, for each of the at least a portion of theat least one training sample, generating predicted full k-space databased on the predicted full MRI image of the training sample. Theupdating the intermediate subsampling model based on the intermediateMRI reconstruction model and at least a portion of the at least onetraining sample may include, for each of the at least a portion of theat least one training sample, obtaining full k-space data based on thefull MRI data of the training sample. The updating the intermediatesubsampling model based on the intermediate MRI reconstruction model andat least a portion of the at least one training sample may include, foreach of the at least a portion of the at least one training sample,generating a comparison result between the predicted full k-space dataand the full k-space data of the training sample. The updating theintermediate subsampling model based on the intermediate MRIreconstruction model and at least a portion of the at least one trainingsample may include updating the intermediate subsampling model based onthe comparison result of each of the at least a portion of the at leastone training sample.

In some embodiments, the intermediate subsampling model may define aplurality of first k-space lines among a plurality of k-space lines. Foreach of the at least a portion of the at least one training sample, thefull k-space data may include first data of each of the plurality ofk-space lines, and the predicted full k-space data may include seconddata of each of the plurality of k-space lines. The generating acomparison result between the predicted full k-space data and the fullk-space data of the training sample may include determining one or moresecond k-space lines by removing the plurality of first k-space linesfrom the plurality of k-space lines. The generating a comparison resultbetween the predicted full k-space data and the full k-space data of thetraining sample may further include, for each of the one or more secondk-space lines, determining a difference between the first data of thesecond k-space line and the second data of the second k-space line, thecomparison result comprising the difference corresponding to the each ofthe one or more second k-space lines.

In some embodiments, in response to determining that the intermediatesubsampling model does not satisfy the termination condition, the atleast one iteration of the one or more iteration may include updating,based on the at least a portion of the at least one training sample, theintermediate MRI reconstruction model.

In some embodiments, the intermediate MRI reconstruction model may be anupdated intermediate subsampling model generated in a previousiteration. In response to determining that the intermediate subsamplingmodel satisfies the termination condition, the at least one iteration ofthe one or more iterations may include designating the intermediatesubsampling model as the subsampling model, and designating theintermediate MRI reconstruction model as the MRI reconstruction model.

In some embodiments, the intermediate MRI reconstruction model may bethe preliminary MRI reconstruction model. In response to determiningthat the intermediate subsampling model satisfies the terminationcondition, the at least one iteration of the one or more iterations mayfurther include designating the intermediate subsampling model as thesubsampling model, and generating the MRI reconstruction model byupdating the intermediate MRI reconstruction model based on the at leasta portion of the at least one training sample.

In some embodiments, the full MRI data of each of the at least onetraining sample may be acquired based on a first MRI sequence. Thesubsampling model may correspond to the first MRI sequence. The at leastone processor may be further configured to direct the system to performthe operations. The operations may include obtaining at least one secondtraining sample each of which includes second full MRI data acquiredbased on a second MRI sequence. The operations may include, for each ofthe at least one second training sample, generating a reference imagecorresponding to the first MRI sequence based on the subsampling modeland the MRI reconstruction model. The operations may also includeobtaining a second preliminary subsampling model and a secondpreliminary MRI reconstruction model. The operations may further includegenerating a second subsampling model and a second MRI reconstructionmodel corresponding to the second MRI sequence by jointly training thesecond preliminary subsampling model and the second preliminary MRIreconstruction model using the at least one second training sample andthe at least one reference image. The second subsampling model may bethe trained second preliminary subsampling model, and the second MRIreconstruction model may be the trained second preliminary MRIreconstruction model.

In some embodiments, for each of the at least one second trainingsample, the generating a reference image based on the subsampling modeland the MRI reconstruction model may include generating a subsampled MRIimage based on the subsampling model and the second full MRI data of thesecond training sample, and generating the reference image by processingthe subsampled MRI image using the MRI reconstruction model.

In some embodiments, the at least one processor may be furtherconfigured to direct the system to perform the operations. Theoperations may include obtaining target subsampled k-space data of asubject by performing an MRI scan on the subject according to thesubsampling model. The operations may include generating a targetsubsampled MRI image of the subject based on the target subsampledk-space data. The operations may further include generating a targetfull MRI image of the subject by processing the target subsampled MRIimage using the MRI reconstruction model.

In some embodiments, the MRI reconstruction model may include at leastone of a convolution network or a generative adversarial network (GAN).

Another aspect of the present disclosure relates to a system forMagnetic Resonance Imaging (MRI). The system may include at least onestorage device including a set of instructions and at least oneprocessor in communication with the at least one storage device. Whenexecuting the set of instructions, the at least one processor may bedirected to perform operations. The operations may include obtainingtarget subsampled k-space data of a subject by performing an MRI scan onthe subject according to a subsampling model corresponding to an MRIreconstruction model. The operations may include generating a targetsubsampled MRI image of the subject based on the target subsampledk-space data. The operations may further include generating a targetfull MRI image of the subject by processing the target subsampled MRIimage using the MRI reconstruction model, wherein the subsampling modeland the MRI reconstruction model are jointly trained using at least onetraining sample.

In some embodiments, the subsampling model and the MRI reconstructionmodel may be jointly trained according to a model training process. Themodel training process may include obtaining the at least one trainingsample each of which includes full MRI data. The model training processmay include obtaining a preliminary subsampling model and a preliminaryMRI reconstruction model. The model training process may further includegenerating the subsampling model corresponding to the MRI reconstructionmodel by jointly training the preliminary subsampling model and thepreliminary MRI reconstruction model using the at least one trainingsample. The subsampling model may be the trained preliminary subsamplingmodel, and the MRI reconstruction model may be at least a portion of thetrained preliminary MRI reconstruction model.

A further aspect of the present disclosure relates to a method forMagnetic Resonance Imaging (MRI). The method may be implemented on acomputing device including at least one processor and at least onestorage device. The method may include obtaining at least one trainingsample each of which includes full MRI data. The method may includeobtaining a preliminary subsampling model and a preliminary MRIreconstruction model. The method may further include generating asubsampling model corresponding to an MRI reconstruction model byjointly training the preliminary subsampling model and the preliminaryMRI reconstruction model using the at least one training sample. Thesubsampling model being the trained preliminary subsampling model, andthe MRI reconstruction model being at least a portion of the trainedpreliminary MRI reconstruction model.

A further aspect of the present disclosure relates to a method forMagnetic Resonance Imaging (MRI). The method may be implemented on acomputing device including at least one processor and at least onestorage device. The method may include obtaining target subsampledk-space data of a subject by performing an MRI scan on the subjectaccording to a subsampling model corresponding to an MRI reconstructionmodel. The method may include generating a target subsampled MRI imageof the subject based on the target subsampled k-space data. The methodmay further include generating a target full MRI image of the subject byprocessing the target subsampled MRI image using the MRI reconstructionmodel, wherein the subsampling model and the MRI reconstruction modelare jointly trained using at least one training sample.

A still further aspect of the present disclosure relates to anon-transitory computer readable medium including executableinstructions. When the executable instructions are executed by at leastone processor, the executable instructions may direct the at least oneprocessor to perform a method. The method may include obtaining at leastone training sample each of which includes full MRI data. The method mayinclude obtaining a preliminary subsampling model and a preliminary MRIreconstruction model. The method may further include generating asubsampling model corresponding to an MRI reconstruction model byjointly training the preliminary subsampling model and the preliminaryMRI reconstruction model using the at least one training sample. Thesubsampling model may be the trained preliminary subsampling model, andthe MRI reconstruction model may be at least a portion of the trainedpreliminary MRI reconstruction model.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary MRI systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generating asubsampling model corresponding to an MRI reconstruction model accordingto some embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for performing acurrent iteration of jointly training a preliminary subsampling modeland a preliminary MRI reconstruction model according to some embodimentsof the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for performing acurrent iteration of jointly training a preliminary subsampling modeland a preliminary MRI reconstruction model according to some embodimentsof the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for determiningwhether an intermediate subsampling model satisfies a terminationcondition in a current iteration according to some embodiments of thepresent disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary process forjointly training a preliminary subsampling model and a preliminary MRIreconstruction model according to some embodiments of the presentdisclosure;

FIG. 10 is a schematic diagram illustrating exemplary intermediatesubsampling models and images generated in process 900 according to someembodiments of the present disclosure;

FIG. 11 is a flowchart of an exemplary process for generatingsubsampling models and MRI reconstruction models of a first MRI sequenceand a second MRI sequence according to some embodiments of the presentdisclosure;

FIG. 12 is a schematic diagram illustrating an exemplary process forjointly training a second preliminary subsampling model and a secondpreliminary MRI reconstruction model corresponding to a second MRIsequence according to some embodiments of the present disclosure;

FIG. 13 illustrates an exemplary process for generating a subsamplingmodel and an MRI reconstruction model for each of a first, a second, anda third MRI sequence according to some embodiments of the presentdisclosure; and

FIG. 14 is a schematic diagram illustrating an exemplary process forgenerating a target full MRI image of a target subject according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “device,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assembly ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices (e.g., processor 210 as illustrated in FIG. 2 ) may be providedon a computer-readable medium, such as a compact disc, a digital videodisc, a flash drive, a magnetic disc, or any other tangible medium, oras a digital download (and can be originally stored in a compressed orinstallable format that needs installation, decompression, or decryptionprior to execution). Such software code may be stored, partially orfully, on a storage device of the executing computing device, forexecution by the computing device. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description mayapply to a system, a device, or a portion thereof.

It will be understood that when a unit, device, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, device, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, device, module, orblock, or an intervening unit, device, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. The term “pixel” and “voxel” in the presentdisclosure are used interchangeably to refer to an element in an image.The term “image” in the present disclosure is used to refer to images ofvarious forms, including a 2-dimensional image, a 3-dimensional image, a4-dimensional image, etc.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the presentdisclosure are described primarily regarding image reconstruction in anMRI system. It should be understood that this is only for illustrationpurposes. The systems and methods of the present disclosure may beapplied to reconstruct image data acquired in different scenarios and/orfor different purposes (e.g., safety monitoring, filming, orphotography) and/or by different image devices (e.g., a computedtomography (CT) scanner, a positron emission tomography (PET) scanner).

For example, the systems and methods of the present disclosure may beapplied to any other kind of medical imaging system. In someembodiments, the imaging system may include a single modality imagingsystem and/or a multi-modality imaging system. The single modalityimaging system may include, for example, the MRI system. Themulti-modality imaging system may include, for example, a computedtomography-magnetic resonance imaging (MRI-CT) system, a positronemission tomography-magnetic resonance imaging (PET-MRI) system, asingle photon emission computed tomography-magnetic resonance imaging(SPECT-MRI) system, a digital subtraction angiography-magnetic resonanceimaging (DSA-MRI) system, etc.

MRI systems are widely used in medical diagnosis and/or treatment byexploiting a powerful magnetic field and radio frequency (RF)techniques. Normally, full k-space data of a subject may need to becollected for reconstructing a full MRI image of the subject. In orderto accelerate the data acquisition and reduce the scan time, a fractionof the full k-space data (i.e., a set of subsampled k-space data) may beacquired by subsampling with, for example, a reduced number of k-spacesampling steps, a reduced number of samples per line, a reduced numberof lines per blade, a reduced number of blades per acquisition, or thelike, or any combination thereof.

In some embodiments, the subsampling approach may be performed accordingto a subsampling model (or referred to as a subsampling pattern). Thesubsampling model may define how to perform subsampling during an MRIscan of the subject. For example, the full k-space data may berepresented as data corresponding to a plurality of k-space lines (e.g.,phase-encoding lines, radial lines). The subsampling model may define aplurality of target k-space lines that need to be sampled among theplurality of k-space lines, the sampling order of the target k-spacelines, or the like, or any combination thereof. In some embodiments,during the MRI scan of the subject, the sampling frequency may be lowerthan twice the highest frequency of MR signals to be sampled.

In some embodiments, after the set of subsampled k-space data of thesubject is acquired during the MRI scan according to the subsamplingmodel, a subsampled MRI image may be generated based on the set ofsubsampled k-space data by, for example, performing an inverse Fouriertransformation on the set of subsampled k-space data. Further, thesubsampled MRI image may need to be reconstructed into a predicted fullMRI image of the subject. Recently, a machine learning technique, suchas a machine learning model, has been utilized for reconstructing thesubsampled MRI image into the predicted full MRI image. However, thesubsampling model used by conventional subsampling approaches maynormally be an arbitrary subsampling model, or a default setting of anMRI system, or set manually by a user (e.g., a doctor, a radiologist) ofthe MRI system. An image reconstructed based on data acquired by thesubsampling model may have a lot of artifacts. In addition, a samesubsampling model may be utilized for different types of machinelearning models, which may lead to a low reconstruction accuracy.

In order to improve the reconstruction accuracy in MRI, an aspect of thepresent disclosure provides systems and methods for generating asubsampling model corresponding to an MRI reconstruction model. Thesystems may obtain at least one training sample each of which includesfull MRI data. The systems may also obtain a preliminary subsamplingmodel and a preliminary MRI reconstruction model. Further, the systemsmay generate the subsampling model corresponding to the MRIreconstruction model by jointly training the preliminary subsamplingmodel and the preliminary MRI reconstruction model using the at leastone training sample. The subsampling model may be the trainedpreliminary subsampling model, and the MRI reconstruction model may beat least a portion of the trained preliminary MRI reconstruction model.

By jointly training the preliminary subsampling model and thepreliminary MRI reconstruction model, the generated subsampling modelmay be regarded as a specific subsampling model that matches and besuitable for the generated MRI reconstruction model. The reliability andthe accuracy of the subsampling model and the MRI reconstruction modelmay be improved. In this way, the sampling efficiency and/or accuracy inan MRI scan performed based on the subsampling model may be improved,and a full MRI image reconstructed based on the MRI reconstruction modelmay have an improved accuracy.

According to some embodiments of the present disclosure, an MRI scan ofa subject may be implemented according to a plurality of MRI sequences.Because that different types of MRI sequences may have differentcharacteristics (e.g., different pulse sequence parameters) and imagesgenerated by different types of MRI sequences may have differentcharacteristics, a specific subsampling model and a specific MRIreconstruction model may be generated for each MRI sequence. This mayfurther improve the accuracy of the generated subsampling models and theMRI reconstruction models of the MRI sequences. Moreover, in someembodiments, during the training process of the subsampling model andthe MRI reconstruction model corresponding to a certain MRI sequence,one or more reference images may be generated based on the trainedsubsampling model(s) and the trained MRI reconstruction model(s)corresponding to other MRI sequence(s). The utilization of the referenceimage(s) may facilitate image reconstruction in the training process ofthe subsampling model and the MRI reconstruction model corresponding tothe certain MRI sequence, and improve the training efficiency (e.g., byaccelerating model convergence) and/or the training accuracy.

FIG. 1 is a schematic diagram illustrating an exemplary MRI system 100according to some embodiments of the present disclosure. It should benoted that the MRI system 100 is merely provided as an exemplary imagingsystem, and not intended to limit the scope of the present disclosure.The exemplary methods described in the present disclosure may be appliedin other imaging systems, such as a CT system, a PET system, a PET-MRIsystem, or the like.

As shown in FIG. 1 , the MRI system 100 may include an MR scanner 110, aprocessing device 120, a storage device 130, one or more terminals 140,and a network 150. In some embodiments, the MR scanner 110, theprocessing device 120, the storage device 130, and/or the terminal(s)140 may be connected to and/or communicate with each other via awireless connection, a wired connection, or a combination thereof. Theconnections between the components in the MRI system 100 may bevariable. For example, the MR scanner 110 may be connected to theprocessing device 120 through the network 150. As another example, theMR scanner 110 may be connected to the processing device 120 directly.

The MR scanner 110 may be configured to scan a subject (or a part of thesubject) to acquire image data, such as MR signals associated with thesubject. For example, the MR scanner 110 may detect a plurality of MRsignals by applying an MRI sequence on the subject. In some embodiments,the MR scanner 110 may include, for example, a magnetic body, a gradientcoil, an RF coil, etc. In some embodiments, the MR scanner 110 may be apermanent magnet MR scanner, a superconducting electromagnet MR scanner,or a resistive electromagnet MR scanner, etc., according to types of themagnetic body. In some embodiments, the MR scanner 110 may be ahigh-field MR scanner, a mid-field MR scanner, and a low-field MRscanner, etc., according to the intensity of the magnetic field.

In the present disclosure, “subject” and “object” are usedinterchangeably. The subject may be biological or non-biological. Forexample, the subject may include a patient, a man-made subject, etc. Asanother example, the subject may include a specific portion, organ,tissue, and/or a physical point of the patient. For example, the subjectmay include head, brain, neck, body, shoulder, arm, thorax, cardiac,stomach, blood vessel, soft tissue, knee, feet, or the like, or acombination thereof.

In some embodiments, the processing device 120 may be a single server ora server group. The server group may be centralized or distributed. Theprocessing device 120 may process data and/or information obtained fromthe MR scanner 110, the storage device 130, and/or the terminal(s) 140.For example, the processing device 120 may obtain at least one trainingsample, and generate a subsampling model corresponding to an MRIreconstruction model by jointly training a preliminary subsampling modeland a preliminary MRI reconstruction model using the at least onetraining sample. As another example, the processing device 120 may applythe subsampling model and the MRI reconstruction model to generate atarget full MRI image of a target subject.

In some embodiments, a trained model (e.g., the subsampling model and/orthe MRI reconstruction model) may be generated by a processing device,while the application of the trained model may be performed on adifferent processing device. In some embodiments, the trained model maybe generated by a processing device of a system different from the MRIsystem 100 or a server different from the processing device 120 on whichthe application of the trained model is performed. For instance, thesubsampling model and/or the MRI reconstruction model may be generatedby a first system of a vendor who provides and/or maintains such atrained model, while the generation of the target full MRI image basedon the provided trained model may be performed on a second system of aclient of the vendor. In some embodiments, the application of thetrained model may be performed online in response to a request forgenerating a target full MRI image of a target subject. In someembodiments, the trained model may be determined or generated offline.

In some embodiments, the trained model may be determined and/or updated(or maintained) by, e.g., the manufacturer of the MR scanner 110 or avendor. For instance, the manufacturer or the vendor may load thesubsampling model and/or the MRI reconstruction model into the MRIsystem 100 or a portion thereof (e.g., the processing device 120) beforeor during the installation of the MR scanner 110 and/or the processingdevice 120, and maintain or update the subsampling model and/or the MRIreconstruction model from time to time (periodically or not). Themaintenance or update may be achieved by installing a program stored ona storage device (e.g., a compact disc, a USB drive, etc.) or retrievedfrom an external source (e.g., a server maintained by the manufactureror vendor) via the network 150. The program may include a new model(e.g., a newly trained model) or a portion of a model that substitute orsupplement a corresponding portion of the model.

In some embodiments, the processing device 120 may be local or remotefrom the MRI system 100. For example, the processing device 120 mayaccess information and/or data from the MR scanner 110, the storagedevice 130, and/or the terminal(s) 140 via the network 150. As anotherexample, the processing device 120 may be directly connected to the MRscanner 110, the terminal(s) 140, and/or the storage device 130 toaccess information and/or data. In some embodiments, the processingdevice 120 may be implemented on a cloud platform. For example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or a combination thereof. In some embodiments,the processing device 120 may be implemented by a computing device 200having one or more components as described in connection with FIG. 2 .

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the MR scanner 110, the processing device 120, and/or theterminal(s) 140. In some embodiments, the storage device 130 may storedata and/or instructions that the processing device 120 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or a combination thereof. Exemplarymass storage devices may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage devices may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memory mayinclude a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), a zero-capacitorRAM (Z-RAM), etc. Exemplary ROM may include a mask ROM (MROM), aprogrammable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), a digital versatile disk ROM, etc. In some embodiments, thestorage device 130 may be implemented on a cloud platform as describedelsewhere in the disclosure.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in the MRIsystem 100 (e.g., the MR scanner 110, the processing device 120, and/orthe terminal(s) 140). One or more components of the MRI system 100 mayaccess the data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be part ofthe processing device 120 or the terminal(s) 140.

The terminal(s) 140 may be configured to enable a user interactionbetween a user and the MRI system 100. For example, the terminal(s) 140may receive an instruction to cause the MR scanner 110 to scan thesubject from the user. As another example, the terminal(s) 140 mayreceive a processing result (e.g., a reconstructed MRI image of a targetsubject) from the processing device 120 and display the processingresult to the user. In some embodiments, the terminal(s) 140 may beconnected to and/or communicate with the MR scanner 110, the processingdevice 120, and/or the storage device 130. In some embodiments, theterminal(s) 140 may include a mobile device 141, a tablet computer 142,a laptop computer 143, or the like, or a combination thereof. Forexample, the mobile device 141 may include a mobile phone, a personaldigital assistant (PDA), a gaming device, a navigation device, a pointof sale (POS) device, a laptop, a tablet computer, a desktop, or thelike, or a combination thereof. In some embodiments, the terminal(s) 140may include an input device, an output device, etc. The input device mayinclude alphanumeric and other keys that may be input via a keyboard, atouch screen (for example, with haptics or tactile feedback), a speechinput, an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to the processing device 120 via, forexample, a bus, for further processing. Other types of the input devicemay include a cursor control device, such as a mouse, a trackball, orcursor direction keys, etc. The output device may include a display, aspeaker, a printer, or the like, or a combination thereof. In someembodiments, the terminal(s) 140 may be part of the processing device120 or the MR scanner 110.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the MRI system 100. In someembodiments, one or more components of the MRI system 100 (e.g., the MRscanner 110, the processing device 120, the storage device 130, theterminal(s) 140, etc.) may communicate information and/or data with oneor more other components of the MRI system 100 via the network 150. Forexample, the processing device 120 may obtain image data (e.g., MRsignals) from the MR scanner 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150.

The network 150 may include a public network (e.g., the Internet), aprivate network (e.g., a local area network (LAN), a wide area network(WAN)), etc.), a wired network (e.g., an Ethernet network), a wirelessnetwork (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellularnetwork (e.g., a Long Term Evolution (LTE) network), a frame relaynetwork, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, or thelike, or a combination thereof. For example, the network 150 may includea cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or a combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the MRI system 100 may beconnected to the network 150 to exchange data and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. In some embodiments, the MRIsystem 100 may include one or more additional components and/or one ormore components described above may be omitted. Additionally oralternatively, two or more components of the MRI system 100 may beintegrated into a single component. For example, the processing device120 may be integrated into the MR scanner 110. As another example, acomponent of the MRI system 100 may be replaced by another componentthat can implement the functions of the component. In some embodiments,the storage device 130 may be a data storage including cloud-computingplatforms, such as a public cloud, a private cloud, a community, andhybrid cloud, etc. However, those variations and modifications do notdepart the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device 200 according to someembodiments of the present disclosure. The computing device 200 may beused to implement any component of the MRI system 100 as describedherein. For example, the processing device 120 and/or a terminal 140 maybe implemented on the computing device 200, respectively, via itshardware, software program, firmware, or a combination thereof. Althoughonly one such computing device is shown, for convenience, the computerfunctions relating to the MRI system 100 as described herein may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load. As illustrated in FIG. 2 , thecomputing device 200 may include a processor 210, a storage device 220,an input/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataof a subject obtained from the MR scanner 110, the storage device 130,terminal(s) 140, and/or any other component of the MRI system 100. Asanother example, the processor 210 may generate an MRI image of thesubject based on the (processed) image data of the subject.

In some embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combination thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. The operations and/or method steps that are performed by oneprocessor as described in the present disclosure may also be jointly orseparately performed by the multiple processors. For example, if in thepresent disclosure the processor of the computing device 200 executesboth operation A and operation B, it should be understood that operationA and operation B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes operation A and a second processor executesoperation B, or the first and second processors jointly executeoperations A and B).

The storage device 220 may store data/information obtained from the MRscanner 110, the storage device 130, the terminal(s) 140, and/or anyother component of the MRI system 100. In some embodiments, the storagedevice 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or a combination thereof. In some embodiments, the storagedevice 220 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure. For example, thestorage device 220 may store a program for the processing device 120 toexecute to generate a trained model (e.g., a subsampling model and/or anMRI reconstruction model). As another example, the storage device 220may store a program for the processing device 120 to execute to applythe trained model (e.g., the subsampling model and/or the MRIreconstruction model) to generate a target full MRI image of a targetsubject.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. The input device may includealphanumeric and other keys that may be input via a keyboard, a touchscreen (for example, with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to another component (e.g., theprocessing device 120) via, for example, a bus, for further processing.Other types of the input device may include a cursor control device,such as a mouse, a trackball, or cursor direction keys, etc. The outputdevice may include a display (e.g., a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), a touch screen),a speaker, a printer, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and oneor more components of the MRI system 100 (e.g., the MR scanner 110, thestorage device 130, and/or the terminal(s) 140). The connection may be awired connection, a wireless connection, any other communicationconnection that can enable data transmission and/or reception, and/or acombination of these connections. The wired connection may include, forexample, an electrical cable, an optical cable, a telephone wire, or thelike, or a combination thereof. The wireless connection may include, forexample, a Bluetooth™ link, a Wi-Fi™ link, a WiMax™ link, a WLAN link, aZigBee link, a mobile network link (e.g., 3G, 4G, 5G, etc.), or thelike, or a combination thereof. In some embodiments, the communicationport 240 may be and/or include a standardized communication port, suchas RS232, RS485, etc. In some embodiments, the communication port 240may be a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device 300 according to some embodimentsof the present disclosure. In some embodiments, one or more componentsof the MRI system 100 may be implemented on one or more components ofthe mobile device 300. Merely by way of example, a terminal 140 may beimplemented on one or more components of the mobile device 300.

As illustrated in FIG. 3 , the mobile device 300 may include acommunication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system 370 (e.g., iOS™, Android™,Windows Phone™, etc.) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating tothe MRI system 100. User interactions with the information stream may beachieved via the I/O 350 and provided to the processing device 120and/or other components of the MRI system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure. Theprocessing devices 120A and 120B may be exemplary embodiments of theprocessing device 120 as described in connection with FIG. 1 . In someembodiments, the processing device 120A may be configured to generate asubsampling model corresponding to an MRI reconstruction model. Theprocessing device 120B may be configured to apply the subsampling modeland the MRI reconstruction model in generating a target full MRI imageof a target subject. In some embodiments, the processing devices 120Aand 120B may be respectively implemented on a processing unit (e.g., aprocessor 210 illustrated in FIG. 2 or a CPU 340 as illustrated in FIG.3 ). Merely by way of example, the processing devices 120A may beimplemented on the computing device 200, and the processing device 120Bmay be implemented on a CPU 340 of a terminal device. Alternatively, theprocessing devices 120A and 120B may be implemented on a same computingdevice 200 or a same CPU 340. For example, the processing devices 120Aand 120B may be implemented on a same computing device 200.

As shown in FIG. 4A, the processing device 120A may include an obtainingmodule 410 and a model generation module 420.

The obtaining module 410 may be configured to obtain information forgenerating the subsampling model and the MRI reconstruction model. Forexample, the obtaining module 410 may obtain at least one trainingsample each of which includes full MRI data. In some embodiments, atraining sample may include full MRI data of a training subject. Thefull MRI data of the training subject may include a full MRI image, fullk-space data, or the like, or any combination thereof. More descriptionsregarding the obtaining of the at least one training sample may be foundelsewhere in the present disclosure. See, e.g., operation 510 in FIG. 5and relevant descriptions thereof. As another example, the obtainingmodule 410 may be configured to obtain a preliminary subsampling modeland a preliminary MRI reconstruction model. The preliminary subsamplingmodel may define a preliminary subsampling pattern before model updatingor training. The preliminary MRI reconstruction model refers to apreliminary algorithm or a preliminary model (e.g., a preliminarymachine learning model) for MRI reconstruction before model training orupdating. More descriptions regarding the obtaining of the preliminarysubsampling model and the preliminary MRI reconstruction model may befound elsewhere in the present disclosure. See, e.g., operation 520 inFIG. 5 and relevant descriptions thereof.

The model generation module 420 may be configured to generate thesubsampling model corresponding to the MRI reconstruction model byjointly training the preliminary subsampling model and the preliminaryMRI reconstruction model using the at least one training sample. Moredescriptions regarding the generation of the subsampling modelcorresponding to the MRI reconstruction model may be found elsewhere inthe present disclosure. See, e.g., operation 530 in FIG. 5 and relevantdescriptions thereof.

As shown in FIG. 4B, the processing device 120B may include an obtainingmodule 430 and an image generation module 440.

The obtaining module 430 may be configured to obtain target subsampledk-space data of a target subject by performing an MRI scan on the targetsubject according to a subsampling model. The subsampling model maydefine how to perform subsampling during an MRI scan of a subject. Moredescriptions regarding the obtaining of the target subsampled k-spacedata of the target subject may be found elsewhere in the presentdisclosure. See, e.g., operation 1410 in FIG. 14 and relevantdescriptions thereof.

The image generation module 440 may be configured to generate a targetsubsampled MRI image of the target subject based on the targetsubsampled k-space data. More descriptions regarding the generation ofthe target subsampled MRI image of the target subject may be foundelsewhere in the present disclosure. See, e.g., operation 1420 in FIG.14 and relevant descriptions thereof. The image generation module 440may be further configured to generate a target full MRI image of thetarget subject by processing the target subsampled MRI image using anMRI reconstruction model. More descriptions regarding the generation ofthe target full MRI image of the target subject may be found elsewherein the present disclosure. See, e.g., operation 1430 in FIG. 14 andrelevant descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the processing device 120A and/or the processing device120B may share two or more of the modules, and any one of the modulesmay be divided into two or more units. For instance, the processingdevices 120A and 120B may share a same obtaining module; that is, theobtaining module 410 and the obtaining module 430 are a same module. Insome embodiments, the processing device 120A and/or the processingdevice 120B may include one or more additional modules, such as astorage module (not shown) for storing data. In some embodiments, theprocessing device 120A and the processing device 120B may be integratedinto one processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process for generating asubsampling model corresponding to an MRI reconstruction model accordingto some embodiment of the present disclosure. In some embodiments,process 500 may be executed by the MRI system 100. For example, theprocess 500 may be implemented as a set of instructions (e.g., anapplication) stored in a storage device (e.g., the storage device 130,the storage device 220, and/or the storage 390). In some embodiments,the processing device 120A (e.g., the processor 210 of the computingdevice 200, the CPU 340 of the mobile device 300, and/or one or moremodules illustrated in FIG. 4A) may execute the set of instructions andmay accordingly be directed to perform the process 500. Alternatively,the process 500 may be performed by a computing device of a system of avendor that provides and/or maintains such a trained model, wherein thesystem of the vendor is different from the MRI system 100. Forillustration purposes, the following descriptions are described withreference to the implementation of the process 500 by the processingdevice 120A, and not intended to limit the scope of the presentdisclosure.

As described elsewhere in this disclosure, a subsampling approach may beutilized in an MRI scan to accelerate the data acquisition and reducethe scan time. For example, a set of subsampled k-space data of thesubject may be acquired during the MRI scan according to a subsamplingmodel (or referred to as a subsampling pattern), and a predicted fullMRI image of the subject may be reconstructed based on the set ofsubsampled k-space data using a machine learning technique (e.g., amachine learning model). However, the subsampling model used byconventional approaches may normally be an arbitrary subsampling model,or a default setting of the MRI system 100, or set manually by a user(e.g., a doctor, a radiologist) of the MRI system 100. In addition, asame subsampling model may be utilized for different types of machinelearning models, which may lead to a low reconstruction accuracy.

In order to improve the sampling efficiency and the reconstructionaccuracy in MRI, an aspect of the present disclosure provides systemsand methods for generating a subsampling model corresponding to an MRIreconstruction model. An MRI reconstruction model refers to a model(e.g., a machine learning model) or an algorithm for MRI reconstruction.For example, the MRI reconstruction model may be configured toreconstruct a subsampled MRI image (or subsampled k-space data) into apredicted full MRI image. In some embodiments, the subsampling model andthe MRI reconstruction model may be jointly generated by performing theprocess 500 as described below, such that the subsampling model may beregarded as a specific subsampling model that matches and be suitablefor the MRI reconstruction model.

In 510, the processing device 120A (e.g., the obtaining module 410) mayobtain at least one training sample each of which includes full MRIdata.

For example, a training sample may include full MRI data of a trainingsubject. As used herein, a training subject may include a biologicalsubject and/or a non-biological subject, such as a patient or a specificportion (e.g., an organ or a tissue) of the patient. The full MRI dataof the training subject may include a full MRI image, full k-space data,or the like, or any combination thereof. The full k-space data may beacquired by performing an MRI scan on the subject based on an MRIsequence. The full MRI image may be reconstructed based on the full MRIdata. In some embodiments, the MRI scan may be performed by an MRIscanner including a plurality of coil units. The full k-space data mayinclude complex data acquired by the coil units or data generated bycombining the complex data acquired by the coil units.

In some embodiments, the training subjects of different training samplesmay correspond to the same human part or different human parts. Forexample, the subsampling model may define a subsampling pattern forcardiac MR scans, and the MRI reconstruction model may be used toreconstruct a cardiac MRI image of a patient. The training subject ofeach training sample may be the heart of a patient.

In some embodiments, the full MRI data of different training samples maybe acquired based on the same type of MRI sequence or different types ofMRI sequences. For example, the full MRI data of different trainingsamples may be acquired based on different types of MRI sequencesincluding, such as, a spin-echo (SE) sequence, an inversion recovery(IR) sequence, a gradient echo (GRE) sequence, an echo-planar imaging(EPI), a fast spin-echo (FSE) sequence, a fluid-attenuated inversionrecovery (FLAIR) sequence, or the like, or a combination thereof. Insuch cases, the generated subsampling model and/or the MRIreconstruction model may have a higher universality and be applicablefor different types of MRI sequences.

As another example, the full MRI data of all training samples may beacquired based on a same MRI sequence, such as a fast spin-echo (FSE)sequence. In such cases, the generated subsampling model and/or the MRIreconstruction model may be specially designed for the fast spin-echo(FSE) sequence. Normally, different MRI sequences may have differentpulse arrangements and be used to acquire different information withrespect to a subject. For example, different MRI sequences may be usedto acquire information relating to different quantitative parameters(e.g., T1, T2, etc.) of the subject. Generating a specializedsubsampling model and/or an MRI reconstruction model for an MRI pulsesequence may improve the sampling efficiency and accuracy in an MRI scanperformed based on the specialized subsampling model and the MRIsequence, and also the reconstruction accuracy of a full MRI imagereconstructed based on the specialized MRI reconstruction mode.

In some embodiments, a training sample of a training subject may bepreviously generated and stored in a storage device (e.g., the storagedevice 130, the storage device 220, the storage 390, or an externaldatabase). The processing device 120A may retrieve the training sampledirectly from the storage device. In some embodiments, at least aportion of the training sample may be generated by the processing device120A. For example, the processing device 120A may obtain MR signals ofthe training subject detected during an MR scan of the training subjectfrom the MR scanner 110, and generate the full k-space data of thetraining subject by filling the MR signals into k-space. As anotherexample, the processing device 120A may further reconstruct the full MRIimage of the training subject based on the full k-space data of thetraining subject.

In 520, the processing device 120A (e.g., the obtaining module 410) mayobtain a preliminary subsampling model and a preliminary MRIreconstruction model.

The preliminary subsampling model may define a preliminary subsamplingpattern before model updating or training. In some embodiments, thepreliminary subsampling model may be determined by the processing device120A, for example, according to a random subsampling algorithm.Alternatively, the preliminary subsampling model may be determinedaccording to a default setting of the MRI system 100 or set manually bya user (e.g., a doctor, a technician, etc.).

The preliminary MRI reconstruction model refers to a preliminaryalgorithm or a preliminary model (e.g., a preliminary machine learningmodel) for MRI reconstruction before model training or updating. In someembodiments, the preliminary MRI reconstruction model may be of amachine learning model, such as a neural network model. For example, thepreliminary MRI reconstruction model may include a Convolutional NeuralNetwork (CNN) model (e.g., a full CNN model, a Le network (LeNet) model,an Alex network (AlexNet) model, a Visual Geometry Group network(VGGNet) model), a recurrent neural network (RNN) model (e.g., abi-directional RNN model, an Elman Neural Network model, a Jordan NeuralNetwork model), a Generative Adversarial network (GAN) model, a U-netmodel, a Resnet model, a residual network model, a cascaded neuralnetwork model, or the like, or any combination thereof.

In some embodiments, the preliminary MRI reconstruction model mayinclude one or more model parameters. For example, the preliminary MRIreconstruction model may be a CNN model and exemplary model parametersof the preliminary MRI reconstruction model may include the number (orcount) of layers, the number (or count) of kernels, a kernel size, astride, a padding of each convolutional layer, a loss function, or thelike, or any combination thereof. Before training, the modelparameter(s) may have their respective initial values. For example, theprocessing device 120A may initialize parameter value(s) of the modelparameter(s) of the preliminary MRI reconstruction model.

In some embodiments, the preliminary MRI reconstruction model may be aGAN model that includes a generator and a discriminator. The generatormay be configured to generate an image based on an input of the GANmodel. The discriminator may be configured to generate a discriminationresult between the image generated by the generator and a ground truthimage (e.g., a full MRI image of a training sample). Alternatively, thepreliminary MRI reconstruction model may be a full CNN model that maylearn a mapping relationship between images. In some embodiments, thefull CNN model may be capable of processing images with any imageresolution or size.

In 530, the processing device 120A (e.g., the model generation module420) may generate the subsampling model corresponding to the MRIreconstruction model by jointly training the preliminary subsamplingmodel and the preliminary MRI reconstruction model using the at leastone training sample. The trained preliminary subsampling model may bedesignated as the subsampling model, and at least a portion of thetrained preliminary MRI reconstruction model may be designated as theMRI reconstruction model. For example, the preliminary MRIreconstruction model may be a GAN model that includes a generator and adiscriminator. A trained generator of a trained GAN may be designated asthe MRI reconstruction model. As yet another example, the preliminaryMRI reconstruction model may be a full CNN model, and a trained full CNNmodel may be designated as the MRI reconstruction model.

In some embodiments, the processing device 120A may update thepreliminary subsampling model and the preliminary MRI reconstructionmodel by performing one or more iterations. For illustration purposes, acurrent iteration of the iteration(s) is described in the followingdescriptions. The current iteration may be performed based on at least aportion of the at least one training sample. A same set or differentsets of training samples may be used in different iterations in trainingthe preliminary subsampling model and the preliminary MRI reconstructionmodel. In some embodiments, the current iteration may include one ormore operations of process 600 as described in connection with FIG. 6and/or one or more operations of process 700 as described in connectionwith FIG. 7 .

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, the MRI reconstruction model and/or thesubsampling model may be stored in a storage device (e.g., the storagedevice 130) disclosed elsewhere in the present disclosure for furtheruse. As another example, after the MRI reconstruction model and thesubsampling model are generated, the processing device 120A may furthertest the MRI reconstruction model and/or the subsampling model using aset of testing samples. As a further example, the processing device 120Amay update the MRI reconstruction model and/or the subsampling modelperiodically or irregularly based on one or more newly-generatedtraining samples (e.g., new full MRI images and/or new full k-space datagenerated in medical diagnosis, etc.).

FIG. 6 is a flowchart illustrating an exemplary process for performing acurrent iteration of jointly training a preliminary subsampling modeland a preliminary MRI reconstruction model according to some embodimentsof the present disclosure. In some embodiments, process 600 may beexecuted by the MRI system 100. For example, the process 600 may beimplemented as a set of instructions (e.g., an application) stored in astorage device (e.g., the storage device 130, the storage device 220,and/or the storage 390). In some embodiments, the processing device 120A(e.g., the processor 210 of the computing device 200, the CPU 340 of themobile device 300, and/or one or more modules illustrated in FIG. 4A)may execute the set of instructions and may accordingly be directed toperform the process 600. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 600 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of process 600illustrated in FIG. 6 and described below is not intended to belimiting.

In some embodiments, the training process of the subsampling model andthe MRI reconstruction model as described in connection with operation530 in FIG. 5 may include one or more iterations. A current iteration ofthe training process may be performed according to the process 600 basedon at least a portion of the at least one training sample. For theconvenience of descriptions, the at least a portion of the at least onetraining sample used in the current iteration is referred to as targettraining sample(s).

In 610, the processing device 120A (e.g., the obtaining module 410) mayobtain an intermediate subsampling model and an intermediate MRIreconstruction model based on the preliminary subsampling model and thepreliminary MRI reconstruction model.

The intermediate subsampling model refers to an initial subsamplingmodel to be processed (e.g., updated or analyzed) in the currentiteration. The intermediate MRI reconstruction model refers to aninitial MRI reconstruction model to be processed (e.g., updated oranalyzed) in the current iteration.

In some embodiments, if the current iteration is the first iterationamong the one or more iterations, the intermediate subsampling model andthe intermediate MRI reconstruction model may be the preliminarysubsampling model and the preliminary MRI reconstruction model asdescribed in connection with operation 520, respectively. If the one ormore iterations include a plurality of iterations and the currentiteration is subsequent to the first iteration among the iterations, theintermediate subsampling model may be an updated intermediatesubsampling model generated in a previous iteration, and theintermediate MRI reconstruction model may be an updated intermediatesubsampling model generated in the previous iteration. For example, ifthe current iteration is the second iteration among the iterations, theintermediate subsampling model may be an updated intermediatesubsampling model generated in the first iteration, and the intermediateMRI reconstruction model may be an updated intermediate subsamplingmodel generated in the first iteration.

In 620, the processing device 120A (e.g., the model generation module420) may determine whether the intermediate subsampling model satisfiesa termination condition.

The termination condition may indicate whether the intermediatesubsampling model is sufficiently updated. For example, the processingdevice 120A may evaluate the accuracy (or reliability) of theintermediate subsampling model based on the target training sample(s)and the intermediate MRI reconstruction model. If the accuracy (orreliability) of the intermediate subsampling model reaches a desiredlevel, the processing device 120A may determine that the intermediatesubsampling model satisfies the termination condition. In someembodiments, the accuracy (or reliability) of the intermediatesubsampling model may be evaluated according to one or more operationsof process 800 as described in connection with FIG. 8 . For example, foreach target training sample, the processing device 120A may generate apredicted full MRI image based on the full MRI data of the targettraining sample, the intermediate subsampling model, and theintermediate MRI reconstruction model. The processing device 120A mayfurther determine whether the intermediate subsampling model satisfiesthe termination condition based on the predicted full MRI image of eachtarget training sample. As another example, the processing device 120Amay determine that the termination condition is satisfied if a specifiednumber (or count) of iterations has been performed in the trainingprocess. It should be noted that the above descriptions of thetermination condition are merely provided for illustration purposes, andnot intended to be limiting.

In response to determining that the intermediate subsampling modelsatisfies the termination condition, the processing device 120A mayproceed to operations 650 and 660. In 650, the processing device 120A(e.g., the model generation module 420) may designate the intermediatesubsampling model as the subsampling model. In 660, the processingdevice 120A (e.g., the model generation module 420) may designate theintermediate MRI reconstruction model as the MRI reconstruction model.

In response to determining that the intermediate subsampling model doesnot satisfy the termination condition, the processing device 120A mayproceed to operations 630 and 640. In 630, the processing device 120A(e.g., the model generation module 420) my update the intermediatesubsampling model based on the intermediate MRI reconstruction model andthe target training sample(s).

Merely by way of example, for each target training sample, theprocessing device 120A may generate predicted full k-space data based onthe predicted full MRI image of the target training sample. For example,the processing device 120A may generate the predicted full k-space dataof a target training sample by performing a Fourier transformation onthe predicted full MRI image of the target training sample. Theprocessing device 120A may also obtain full k-space data of the targettraining sample based on the full MRI data of the target trainingsample. The full k-space data of the target training sample may beincluded in the target training sample or be transformed from a full MRIimage of the target training sample. Then, for each target trainingsample, the processing device 120A may generate a comparison resultbetween the predicted full k-space data and the full k-space data of thetarget training sample. The processing device 120A may further updatethe intermediate subsampling model based on the comparison result ofeach target training sample.

The comparison result of a target training sample may indicate adifference between the predicted full k-space data and the full k-spacedata of the target training sample. For example, the difference may bedetermined by subtracting the predicted full k-space data from the fullk-space data of the target training sample. As another example, thedifference may be determined based on the predicted full k-space dataand the full k-space data according to a least-square algorithm.

In some embodiments, the intermediate subsampling model may define aplurality of first k-space lines that need to be sampled among aplurality of k-space lines in full k-space. The full k-space data of thetarget training sample may include first data of each of the pluralityof k-space lines, and the predicted full k-space data may include seconddata of each of the plurality of k-space lines. The processing device120A may determine one or more second k-space lines by removing theplurality of first k-space lines from the plurality of k-space lines. Inother words, the second k-space lines that are omitted from samplingaccording to the intermediate subsampling model may be determined. Foreach of the second k-space line(s), the processing device 120A maydetermine a difference between the first data and the second data of thesecond k-space line. The comparison result of the target training samplemay include the difference corresponding to each of the one or moresecond k-space lines. For a certain second k-space line, the differencebetween the corresponding first and second data may be determined bysubtracting the first data from the second data or subtracting thesecond data from the first data. Alternatively, the difference betweenthe corresponding first and second data may be measured by, for example,an L1 norm difference, an L2 norm difference, a covariance value, or thelike, or any combination thereof, of the first data and the second data.

In some embodiments, the processing device 120A may update theintermediate subsampling model according to the difference(s)corresponding to the one or more second k-space lines of each targettraining sample. For example, for each target training sample, theprocessing device 120A may select one or more second k-space lines withthe largest X differences among the second k-space line(s), wherein Xmay be equal to any positive integer (e.g., 1, 2, 3, 5, etc.). Theprocessing device 120A may update the intermediate subsampling model byadding the selected second k-space line(s) of each target trainingsample into the intermediate subsampling model. As another example, theprocessing device 120A may mark the one or more second k-space lineswith the largest X differences for each training sample. The processingdevice 120A may determine the number of times that each second k-spaceline is marked, and select one or more second k-space lines having thetop Y numbers of times (Y being any positive integer) among the secondk-space line(s). The processing device 120A may further update theintermediate subsampling model by adding the selected second k-spaceline(s) into the intermediate subsampling model. In other words, theselected second k-space line(s) may be deemed as important k-space linesthat need to be sampled, and the updated intermediate subsampling modelmay include both the first k-space line(s) and the selected secondk-space line(s).

In 640, the processing device 120A (e.g., the model generation module420) may update the intermediate MRI reconstruction model.

In some embodiments, the processing device 120A may determine a value ofa loss function based on the intermediate subsampling model, theintermediate MRI reconstruction model, and the target trainingsample(s). The processing device 120 may further update value(s) of themodel parameter(s) of the intermediate MRI reconstruction model based onthe value of the loss function according to, for example, abackpropagation algorithm. The loss function may be used to evaluate thereliability and/or accuracy of the intermediate MRI reconstruction modelin the current iteration, for example, the smaller the loss function is,the more reliable the intermediate MRI reconstruction model is.Exemplary loss functions may include an L1 loss function, a focal lossfunction, a log loss function, a cross-entropy loss function, a Diceloss function, etc. In some embodiments, the loss function may bedetermined by comparing the predicted full k-space data and the fullk-space data of each target training sample. As another example, theloss function may be determined by comparing the predicted full MRIimage and the full MRI image of each target training sample.

In some embodiments, the processing device 120A may update theintermediate MRI reconstruction model by performing a second iterativeprocess including one or more second iterations until a secondtermination condition is satisfied in a certain second iteration. Forexample, the second termination condition may be that the value of theloss function is less than a threshold. The threshold may be defaultsettings of the MRI system 100 or be adjustable under differentsituations. As another example, the second termination condition may besatisfied if the value of the cost function converges. The convergencemay be deemed to have occurred if the variation of the values of thecost function in two or more consecutive iterations is smaller than athreshold (e.g., a constant). As still another example, the processingdevice 120A may determine that the second termination condition issatisfied if a specified number (or count) of second iterations has beenperformed.

In some embodiments, after 640, the processing device 120A may proceedto operation 610 to perform the next iteration until the terminationcondition is satisfied. The next iteration may be performed based on asame set or a different set of target training sample(s). After thetermination condition is satisfied in a certain iteration, theintermediate subsampling model in the certain iteration may bedesignated as the subsampling model, and the intermediate MRIreconstruction model in the certain iteration may be designated as theMRI reconstruction model.

In some embodiments, the current iteration of jointly training thepreliminary subsampling model and the preliminary MRI reconstructionmodel may be performed by process 700 as shown in FIG. 7 . The process700 may be performed in a similar manner as the process 600, except forcertain features. As described in connection with FIG. 6 , in the firstiteration of the training process, the intermediate MRI reconstructionmodel may be the preliminary MRI reconstruction model; in an iterationsubsequent to the first iteration, the intermediate MRI reconstructionmodel may be an updated intermediate MRI reconstruction model generatedin a previous iteration. If it is determined that the intermediatesubsampling model does not satisfy the termination condition in thecurrent iteration, the intermediate MRI reconstruction model may beupdated. In other words, the value(s) of the model parameter(s) of thepreliminary MRI reconstruction model determined in a previous iterationmay be “inherited” to a next iteration, and the preliminary MRIreconstruction model may be updated for multiple times in the trainingprocess of FIG. 6 .

Different from the process 600, the intermediate MRI reconstructionmodel obtained in each iteration (e.g., the first iteration, aniteration subsequent to the first iteration, etc.) may both be thepreliminary MRI reconstruction model. If it is determined that theintermediate subsampling model does not satisfy the terminationcondition in the current iteration, the training process may proceed toa next iteration without updating the intermediate MRI reconstructionmodel. If it is determined that the intermediate subsampling modelsatisfies the termination condition in the current iteration, theintermediate MRI reconstruction model may be updated to generate the MRIreconstruction model.

The process 600 and the process 700 may have their respectiveadvantages, and the processing device 120A or a user of the MRI system100 may select one of them to jointly generate the subsampling model andthe MRI reconstruction according to an actual situation. For example, inthe process 600, the preliminary MRI reconstruction model may be updatedmultiple times, thereby generating an MRI reconstruction model with ahigher accuracy and reliability. In the process 700, the preliminary MRIreconstruction model may not need to be updated during each iteration,which may save the training time and improve the training efficiency.

In 710, the processing device 120A (e.g., the obtaining module 410) mayobtain an intermediate subsampling model and an intermediate MRIreconstruction model.

Operation 710 may be performed in a similar manner as operation 610 asdescribed in connection with FIG. 6 , except that the intermediate MRIreconstruction model obtained in the first iteration or an iterationsubsequent to the first iteration may both be the preliminary MRIreconstruction model.

In 720, the processing device 120A (e.g., the model generation module420) may determine whether the intermediate subsampling model satisfiesa termination condition. Operation 720 may be performed in a similarmanner as operation 620 as described in connection with FIG. 6 , and thedescriptions thereof are not repeated here.

In response to determining that the intermediate subsampling model doesnot satisfy the termination condition, the processing device 120A mayproceed to operation 730.

In 730, the processing device 120A (e.g., the model generation module420) my update the intermediate subsampling model. Operation 730 may beperformed in a similar manner as operation 630 as described inconnection with FIG. 6 , and the descriptions thereof are not repeatedhere.

In some embodiments, after 730, the processing device 120A may proceedto operation 710 to perform the next iteration until the terminationcondition is satisfied. The next iteration may be performed based on asame set or different set of target training sample(s). After thetermination condition is satisfied in a certain iteration, theintermediate subsampling model in the certain iteration may bedesignated as the subsampling model.

In response to determining that the intermediate subsampling modelsatisfies the termination condition, the processing device 120A mayproceed to operations 740 and 750.

In 740, the processing device 120A (e.g., the model generation module420) may designate the intermediate subsampling model as the subsamplingmodel. In 750, the processing device 120A (e.g., the model generationmodule 420) may generate the MRI reconstruction model by updating theintermediate MRI reconstruction model based on the target trainingsample(s). In some embodiments, the processing device 120A may updatethe intermediate MRI reconstruction model by performing a seconditerative process as described in connection with 640.

It should be noted that the above descriptions regarding the processes600 and 700 are merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. In some embodiments, one or more operations may beadded or omitted. For example, one or more other optional operations(e.g., a storing operation) may be added in the processes 600 and 700.In the storing operation, the processing device 120A may storeinformation and/or data (e.g., a training sample, the subsampling model,the MRI reconstruction model, etc.) associated with the MRI system 100in a storage device (e.g., the storage device 130) disclosed elsewherein the present disclosure.

FIG. 8 is a flowchart illustrating an exemplary process for determiningwhether an intermediate subsampling model satisfies a terminationcondition in a current iteration according to some embodiments of thepresent disclosure. In some embodiments, process 800 may be executed bythe MRI system 100. For example, the process 800 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 130, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 120A (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4A) mayexecute the set of instructions and may accordingly be directed toperform the process 800. The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 800 may be accomplished with one or more additionaloperations not described and/or without one or more of the operationsdiscussed. Additionally, the order of the operations of process 800illustrated in FIG. 8 and described below is not intended to belimiting. In some embodiments, one or more operations of the process 800may be performed to achieve at least part of operation 620 in FIG. 6and/or operation 720 in FIG. 7 .

In 810, for each of the at least a portion of the at least one trainingsample (i.e., each target training sample), the processing device 120A(e.g., the model generation module 420) may generate a subsampled MRIimage based on the intermediate subsampling model obtained in thecurrent iteration and full MRI data of the training sample.

As described elsewhere in this disclosure (e.g., FIG. 5 and the relevantdescriptions), the full MRI data of a target training sample may a fullMRI image, full k-space data, or the like, or any combination thereof.The processing device 120A may determine full k-space data of the targettraining sample based on the full MRI data of the target trainingsample. The processing device 120A may then determine a set ofsubsampled k-space data from the full k-space data of the targettraining sample according to the intermediate subsampling model. Theprocessing device 120A may further generate the subsampled MRI image ofthe target training sample based on the set of subsampled k-space data.

Merely by way of example, the full k-space data of the target trainingsample may be represented as data corresponding to a plurality ofk-space lines (e.g., phase-encoding lines, radial lines). Theintermediate subsampling model may define a plurality of target k-spacelines (or referred to as first k-space lines) that need to be sampledamong the plurality of k-space lines. The processing device 120A mayextract a portion of the full k-space data that corresponds to thetarget k-space lines, and designate the extracted portion of the fullk-space data as the set of subsampled k-space data. As another example,the processing device 120A may generate the set of subsampled k-spacedata by performing a dot product on the intermediate subsampling modeland the full k-space data of the target training sample. The processingdevice 120A may further generate the subsampled MRI image by performingan inverse Fourier transformation on the set of subsampled k-space data.

In 820, for each of the at least a portion of the at least one trainingsample (i.e., each target training sample), the processing device 120A(e.g., the model generation module 420) may generate, based on thesubsampled MRI image and the intermediate MRI reconstruction model, apredicted full MRI image.

For example, for a target training sample, the corresponding subsampledMRI image may be inputted into the intermediate MRI reconstructionmodel, and the intermediate MRI reconstruction model may output thepredicted full MRI image of the target training sample. As anotherexample, the subsampled MRI image may be preprocessed (e.g., resampled,normalized, smoothed) before being inputted into the intermediate MRIreconstruction model. As yet another example, the intermediate MRIreconstruction model may generate an output in response to the input(e.g., the predicted full MRI image or the preprocessed predicted fullMRI image), and the processing device 120A may post-process (e.g.,resample, denormalize) the output to generate the predicted full MRIimage. In some embodiments, the intermediate MRI reconstruction modelmay perform one or more processing operations (e.g., a convolutionoperation, a pooling operation, a feature extraction operation, or thelike, or any combination thereof) on its input.

In 830, for each of the at least a portion of the at least one trainingsample (i.e., each target training sample), the processing device 120A(e.g., the model generation module 420) may generate a determinationresult of whether the predicted full MRI image satisfies a presetcondition.

For example, for a target training sample, the processing device 120Amay obtain a full MRI image based on the full MRI data of the targettraining sample, and determine a difference (or a degree of similarity)between the full MRI image and the predicted full MRI image of thetarget training sample.

As used herein, the difference between two images may be measured by oneor more algorithms for image difference (or similarity) measurement.Exemplary algorithms for image difference (or similarity) measurementmay include a Peak Signal to Noise Ratio (PSNR) algorithm, a StructuralSimilarity Index (SSIM) algorithm, a histogram algorithm, a perceptualhash algorithm, or the like, or any combination thereof. Merely by wayof example, first value(s) of one or more parameters (e.g., thelightness, the contract ratio, the structure, the PSNR, the SSIM) of thepredicted full MRI image may be determined. Second value(s) of theparameter(s) of the full MRI image may be determined. Difference(s)between the first value(s) and the second value(s) may be furtherdetermined to determine the difference between the two images. In someembodiments, a plurality of parameters may be utilized to determine thedifference. For each of the parameters, the processing device 120A maydetermine a difference between the first value and the second value ofthe parameter. The processing device 120A may further determine thedifference between the two images by determining a weighted sum of thedifferences corresponding to the parameters.

Further, for the target training sample, the processing device 120A maydetermine whether the predicted full MRI image of the target trainingsample satisfies the preset condition by determining whether thedifference exceeds a threshold difference. The threshold difference maybe determined according to a default setting of the MRI system 100, orautomatically by the processing device 120A according to an actual need,or set manually by the user (e.g., a doctor, a technician, etc.). If thedifference between the predicted full MRI image and the full MRI imagedoes not exceed the threshold difference, the processing device 120A maygenerate a determination result that the predicted full MRI image of thetarget training sample satisfies the preset condition. If the differencebetween the predicted full MRI image and the full MRI image exceeds thethreshold difference, the processing device 120A may generate adetermination result that the predicted full MRI image of the targettraining sample does not satisfy the preset condition.

In 840, the processing device 120A (e.g., the model generation module420) may determine, based on the determination result of each of the atleast a portion of the at least one training sample (i.e., each targettraining sample), whether the intermediate subsampling model satisfiesthe termination condition.

In some embodiments, the target training sample(s) may include onetarget training sample. If the predicted full MRI image of the targettraining sample satisfies the preset condition, the processing device120A may determine that the intermediate subsampling model satisfies thetermination condition. If the predicted full MRI image of the targettraining sample does not satisfy the preset condition, the processingdevice 120A may determine that the intermediate subsampling model doesnot satisfy the termination condition.

In some embodiments, if the target training sample(s) include aplurality of target training samples, the processing device 120A maydetermine whether the intermediate subsampling model satisfies thetermination condition based on the determination results of the targettraining samples. For example, the processing device 120A may determinethe count of target training samples whose predicted full MRI imagesatisfies the preset condition. If the count exceeds a certainpercentage (e.g., 60%, 70%, 80%, 90%, etc.) of the total count of thetarget training samples, the processing device 120A may determine thatthe intermediate subsampling model satisfies the termination condition.If the count does not exceed the certain percentage of the total countof the target training samples, the processing device 120A may determinethat the intermediate subsampling model does not satisfy the terminationcondition.

It should be noted that the above description regarding the process 800is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. In some embodiments, whether the intermediate subsampling modelsatisfies the termination condition may be determined by anotherapproach, for example, manually by a user.

FIG. 9 is a schematic diagram illustrating an exemplary process forjointly training a preliminary subsampling model and a preliminary MRIreconstruction model according to some embodiments of the presentdisclosure. FIG. 10 is a schematic diagram illustrating exemplaryintermediate subsampling models and images generated in the process 900according to some embodiments of the present disclosure. The process 900may be an exemplary embodiment of the process 500 as described in FIG. 5.

As shown in FIGS. 9 and 10 , the training process of the preliminarysubsampling model and the preliminary MRI reconstruction model mayinclude N iteration, wherein N may be any positive integer. Sirepresents an intermediate subsampling model of an i^(th) iteration. Rirepresents an intermediate MRI reconstruction model of the i^(th)iteration. Mi represents a subsampled MRI image of a training samplegenerated in the i^(th) iteration. Mi′ represents a predicted full MRIimage of a training sample generated in the i^(th) iteration. Eachtraining sample may include full MRI data, such as full k-space dataand/or a full MRI image of a training subject. For illustrationpurposes, the following descriptions describe the training process ofthe preliminary subsampling model and the preliminary MRI reconstructionmodel based on one training sample (or referred to as a target trainingsample). It should be noted that this is not intended to be limiting,and the training process may be implemented on a plurality of trainingsamples.

In the first iteration (i.e., the iteration 1 in FIGS. 9 and 10 ), theintermediate subsampling model S1 may be the preliminary subsamplingmodel as described in connection with operation 520. The intermediateMRI reconstruction model R1 may be the preliminary MRI reconstructionmodel as described in connection with operation 520. For a trainingsample, a subsampled MRI image M1 may be generated based on theintermediate subsampling model S1 and the full MRI data of the trainingsample. The subsampled MRI image M1 may be processed by an intermediateMRI reconstruction model R1 to generate a predicted full MRI image M1′.

Based on the predicted full MRI image M1′ and the full MRI data of thetraining sample, whether the intermediate subsampling model S1 satisfiesa termination condition may be determined. For example, a full MRI imageof the training sample may be determined based on the full MRI data ofthe training sample. The predicted full MRI image M1′ may be comparedwith the full MRI image to determine a difference between the predictedfull MRI image M1′ and the full MRI image. If the difference exceeds athreshold difference, it may be determined that the intermediatesubsampling model S1 does not satisfy the termination condition. Inresponse to determining that the intermediate subsampling model S1 doesnot satisfy the termination condition, the intermediate subsamplingmodel S1 may be updated by performing 630 as described in connectionwith FIG. 6 to generate an intermediate subsampling model S2, and theintermediate MRI reconstruction model R1 may be updated by performing640 as described in connection with FIG. 6 to generate an intermediateMRI reconstruction model R2.

In the second iteration (i.e., the iteration 2 in FIGS. 9 and 10 ), theintermediate subsampling model S2 may be the updated intermediatesubsampling model S1 generated in the first iteration, and theintermediate MRI reconstruction model R2 may be the updated intermediateMRI reconstruction model R1 generated in the first iteration. The seconditeration may be implemented in a similar manner as the first iteration,and the descriptions thereof are not repeated here. If it is determinedthe intermediate subsampling model S2 does not satisfy the terminationcondition, the intermediate subsampling model S2 and the intermediateMRI reconstruction model R2 may be further updated.

The training process may be terminated if the intermediate subsamplingmodel in a certain iteration satisfies the termination condition. Theintermediate subsampling model in the certain iteration may bedesignated as the subsampling model, and the intermediate MRIreconstruction model in the certain iteration may be designated as theMRI reconstruction model.

Referring to FIG. 10 , in some embodiments, a training sample mayinclude a full MRI image of the brain of a patient. The intermediatesubsampling model Si of the i^(th) iteration may define a plurality ofk-space lines that need to be sampled. The subsampled MRI image Migenerated in the i^(th) iteration may be an aliasing image. Thesubsampled MRI image Mi may be processed by an intermediate MRIreconstruction model of the i^(th) iteration (not shown in FIG. 10 ) togenerate a predicted full MRI image Mi′, which is a non-aliasing image.

With the implementation of the training process, the count of k-spacelines of the intermediate subsampling model in an iteration (S1-S_(N) inFIG. 10 ) may be increased, the similarity between the generatedsubsampled MRI image of the iteration (M1-Mn in FIG. 10 ) and the fullMRI image may be increased, and the similarity between the predictedfull MRI image of the iteration (M1′-Mn′ in FIG. 10 ) and the full MRIimage may be increased. This suggests that during the training process,the accuracy of the preliminary subsampling model and the preliminaryMRI reconstruction model may be gradually improved.

It should be noted that the above description regarding the process 900is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, one or more operations of the process 900described above may be omitted. As another example, the intermediate MRIreconstruction model Ri in the i^(th) iteration (e.g., the firstiteration, an iteration subsequent to the first iteration, etc.) may bethe preliminary MRI reconstruction model. If it is determined that theintermediate subsampling model Si does not satisfy the terminationcondition in the i^(th) iteration, only the intermediate subsamplingmodel Si may be updated, and the training process may proceed to a nextiteration without updating the intermediate MRI reconstruction model Ri.If it is determined that the intermediate subsampling model Si satisfiesthe termination condition in the i^(th) iteration, the intermediate MRIreconstruction model Ri may be updated to generate the MRIreconstruction model.

In some embodiments, the processing device 120A may first update themodel parameter(s) of the preliminary MRI reconstruction model togenerate the MRI reconstruction model. Further, the processing device120A may update the preliminary subsampling model based on the MRIreconstruction model and the at least one training sample. For example,the updating of the preliminary subsampling model may be performed in asimilar manner as that as shown in FIG. 9 , except that the intermediateMRI reconstruction model in each iteration may be replaced by the MRIreconstruction model and each iteration may be performed withoutupdating the MRI reconstruction model.

In some embodiments, a plurality of MRI sequences may be performed on asubject to acquire different information of the subject. For example, anMRI sequence may be applied to the subject to generate a T1-weight imageof the subject, and another MRI sequence may be applied to the subjectto generate a T2-weight image of the subject. Exemplary MRI sequencesmay include a spin-echo (SE) sequence, an inversion recovery (IR)sequence, a gradient echo (GRE) sequence, an echo-planar imaging (EPI),a fast spin-echo (FSE) sequence, a fluid-attenuated inversion recovery(FLAIR) sequence, or the like, or a combination thereof.

In some embodiments, a universal subsampling model and/or a universalMRI reconstruction model may be generated for the plurality of MRIsequences. For example, a plurality of training samples, which includefull MRI data acquired by the plurality of MRI sequences, may be used togenerate the universal subsampling model and the universal MRIreconstruction model by performing the process 500 as described inconnection with FIG. 5 . In some embodiments, considering that differenttypes of MRI sequences may have different characteristics (e.g.,different pulse sequence parameters), a specific subsampling model and aspecific MRI reconstruction model may be trained for each MRI sequence,in order to improve the accuracy of the generated subsampling models andthe MRI reconstruction models of the MRI sequences.

For illustration purposes, FIG. 11 illustrates is a flowchart of anexemplary process for generating subsampling models and MRIreconstruction models of a first MRI sequence and a second MRI sequenceaccording to some embodiments of the present disclosure. The first MRIsequence and the second MRI sequence may be of different types.

In 1110, the processing device 120A (e.g., the model generation module420) may obtain a first subsampling model (or referred to as asubsampling model) and a first MRI reconstruction model (or referred toas an MRI reconstruction model) corresponding to a first MRI sequence(or referred to as an MRI sequence).

In some embodiments, the processing device 120A may generate the firstsubsampling model and the first MRI reconstruction model based on atleast one first training sample by performing process 500 as describedin connection with FIG. 5 . For example, the processing device 120A mayobtain at least one first training sample each of which includes firstfull MRI data of a first training subject, wherein the first full MRIdata may be acquired based on the first MRI sequence. The processingdevice 120A may further obtain a first preliminary subsampling model anda first preliminary MRI reconstruction model. The processing device 120Amay also generate the first subsampling model and the first MRIreconstruction model by jointly training the first preliminarysubsampling model and the first preliminary MRI reconstruction modelusing the at least one first training sample. The first subsamplingmodel may be the trained first preliminary subsampling model, and thefirst MRI reconstruction model may be at least a portion of the trainedfirst preliminary MRI reconstruction model.

As another example, the first subsampling model and the first MRIreconstruction model may be previously generated and stored in a storagedevice (e.g., the storage device 130, a storage device of theterminals(s) 140, or an external storage device). The processing device120A may obtain the first subsampling model and the first MRIreconstruction model from the storage device.

In 1120, the processing device 120A (e.g., the model generation module420) may obtain at least one second training sample each of whichincludes second full MRI data acquired based on a second MRI sequence.

In some embodiments, the second full MRI data of a second trainingsample may include second full k-space data and/or a second full MRIimage of a second training subject. In some embodiments, a firsttraining sample and a second training sample may include full MRI dataof a same training subject or different training subjects. That is, thefirst training subject of a first training sample may be the same as ordifferent from the second training subject of a second training sample.For example, a first training sample and a second training sample mayinclude different sets of full MRI data of a same patient acquired usingdifferent MRI sequences.

In 1130, for each second training sample, the processing device 120A(e.g., the model generation module 420) may generate a reference imagecorresponding to the first MRI sequence based on the first subsamplingmodel and the first MRI reconstruction model.

In some embodiments, for a second training sample, the processing device120A may generate a second subsampled MRI image based on the firstsubsampling model and the second full MRI data of the second trainingsample. The processing device 120A may further generate the referenceimage by processing the second subsampled MRI image using the MRIreconstruction model. For example, the processing device 120A may obtaina second set of subsampled k-space data of the second training sample byapplying the first subsampling model on the second full k-space data ofthe second training sample. Then, the second subsampled MRI image of thesecond training sample may be generated based on the second set ofsubsampled k-space data. Further, the processing device 120A maygenerate the second predicted full MRI image of the second trainingsample using the first MRI reconstruction model. The second predictedfull MRI image of the second training sample may be designated as thereference image of the second training sample. In some embodiments, thegeneration of a second subsampled MRI image based on the second full MRIdata of a second training sample and the first subsampling model may beperformed in a similar manner as the generation of a subsampled MRIimage based on full MRI data of a training sample and an intermediatesubsampling model as described in connection with 810. The generation ofthe second predicted full MRI image of the second training sample basedon the second subsampled MRI image and the first MRI reconstructionmodel may be performed in a similar manner as the generation of apredicted full MRI image of a training sample based on the subsampledMRI image and an intermediate MRI reconstruction model as described inconnection with 820.

In some embodiments, the second training subject of a second trainingsample may be the same as the first training subject of a first trainingsample. In the last iteration of an iterative training process of thefirst subsampling model and the first MRI reconstruction model, apredicted full MRI image of the first training sample (e.g., thepredicted full MRI image Mn′ as shown in FIG. 9 ) may be generated. Thepredicted full MRI image of the first training sample generated in thelast iteration may be obtained as the reference image of the secondtraining sample.

In 1140, the processing device 120A (e.g., the obtaining module 410) mayobtain a second preliminary subsampling model and a second preliminaryMRI reconstruction model.

The second preliminary subsampling model may define a preliminarysubsampling pattern corresponding to the second MRI sequence beforemodel updating or training. In some embodiments, the obtaining of thesecond preliminary subsampling model may be performed in a similarmanner as that of a preliminary subsampling model as described inconnection with operation 520 in FIG. 5 .

The second preliminary MRI reconstruction model refers to a preliminaryalgorithm or a preliminary model (e.g., a preliminary machine learningmodel) for MRI reconstruction corresponding to the second MRI sequencebefore model training or updating. In some embodiments, the secondpreliminary MRI reconstruction model may be of the same type as or adifferent type from the preliminary MRI reconstruction model asdescribed in connection with operation 520. In some embodiments, theobtaining of the second preliminary MRI reconstruction model may beperformed in a similar manner as that of a preliminary MRIreconstruction model as described in connection with operation 520.

In 1150, the processing device 120A (e.g., the model generation module420) may generate a second subsampling model and a second MRIreconstruction model corresponding to the second MRI sequence by jointlytraining the second preliminary subsampling model and the secondpreliminary MRI reconstruction model using the at least one secondtraining sample and the at least one reference image. The secondsubsampling model may be the trained second preliminary subsamplingmodel, and the second MRI reconstruction model may be at least a portionof the trained second preliminary MRI reconstruction model.

In some embodiments, the training process of the second preliminarysubsampling model and the second preliminary MRI reconstruction model(referred to as a second training process for the convenience ofdescriptions) may be performed in a similar manner as the trainingprocess of the preliminary subsampling model and the preliminary MRIreconstruction model as described in connection with operation 530,except that the reference image(s) generated in operation 1140 may beused in the second training process.

For illustration purposes, FIG. 12 illustrates a schematic diagramshowing an exemplary process for jointly training the second preliminarysubsampling model and the second preliminary MRI reconstruction model(i.e., the second training process) according to some embodiments of thepresent disclosure. One or more operations of process 1200 in FIG. 12may be performed to achieve at least part of operation 1150.

As shown in FIG. 12 , the second training process may include niteration, wherein n may be any positive integer, and n may be the sameas or different from N as described in connection with FIG. 9 . In FIG.12 , s_(i) represents an intermediate subsampling model of an i^(th)iteration in the second training process; r_(i) represents anintermediate MRI reconstruction model of the i^(th) iteration; m_(i)represents a subsampled MRI image of a second training sample generatedin the i^(th) iteration; and m_(i)′ represents a predicted full MRIimage of a second training sample generated in the i^(th) iteration.Each second training sample may include second full MRI data, such assecond full k-space data and/or a second full MRI image of a secondtraining subject. For illustration purposes, the following descriptionsdescribe the second training process based on one second trainingsample. It should be noted that this is not intended to be limiting, andthe training process may be implemented on a plurality of secondtraining samples.

In the first iteration (i.e., the iteration 1 in FIG. 12 ) of the secondtraining process, the intermediate subsampling model s1 may be thesecond preliminary subsampling model as described in connection withoperation 1140. The intermediate MRI reconstruction model r1 may be thesecond preliminary MRI reconstruction model as described in connectionwith operation 1140. For a second training sample, a subsampled MRIimage m1 may be generated based on the intermediate subsampling model s1and the second full MRI data of the second training sample. Thesubsampled MRI image s1 and a reference image of the second trainingsample may be processed by the intermediate MRI reconstruction model r1to generate a predicted full MRI image m1′. In some embodiments, theintermediate MRI reconstruction model r1 may be a GAN model including agenerator and a discriminator. The generator may be configured totransform the subsampled MRI image s1 and the reference image intonon-aliasing images. The discriminator may be configured to determinewhether an image is a second full MRI image or an image generated by thegenerator.

Based on the predicted full MRI image m1′ and the second full MRI data,whether the intermediate subsampling model s1 satisfies a thirdtermination condition may be determined. The third termination conditionmay be similar to the termination condition as described in connectionwith FIG. 9 . The determination as to whether the intermediatesubsampling model s1 satisfies the third termination condition may beperformed in a similar manner as the determination as to whether theintermediate subsampling model S1 satisfies the termination condition asdescribed in connection with FIG. 9 . In response to determining thatthe intermediate subsampling model s1 does not satisfy the thirdtermination condition, the intermediate subsampling model s1 may beupdated to generate an intermediate subsampling model s2, and theintermediate MRI reconstruction model r1 may be updated to generate anintermediate MRI reconstruction model r2. The update of the intermediatesubsampling model s1 and the intermediate MRI reconstruction model r1may be performed in a similar manner as that of the intermediatesubsampling model S1 and the intermediate MRI reconstruction model R1,respectively, as described in connection with FIG. 9 .

In the second iteration (i.e., the iteration 2 in FIG. 12 ) of thesecond training process, the intermediate subsampling model s2 may bethe updated intermediate subsampling model s1 generated in the firstiteration of the second training process, and the intermediate MRIreconstruction model r2 may be the updated intermediate MRIreconstruction model r1 generated in the first iteration. The seconditeration may be implemented in a similar manner as the first iteration,and the descriptions thereof are not repeated herein. If it isdetermined the intermediate subsampling model s2 does not satisfy thethird termination condition, the intermediate subsampling model s2 andthe intermediate MRI reconstruction model r2 may be further updated.

The second training process may be terminated if the intermediatesubsampling model in a certain iteration satisfies the third terminationcondition. The intermediate subsampling model in the certain iterationof the second training process may be designated as the secondsubsampling model, and the intermediate MRI reconstruction model in thecertain iteration may be designated as the second MRI reconstructionmodel.

Compared with the training process as described in connection with FIGS.5 to 9 , the second training process may be different in that in eachiteration of the second training process, the predicted full MRI imageof a second training sample may be determined based on the subsampledMRI image of the second training sample and also the reference image ofthe second training sample. The reference image may include structureinformation of the training subject of the second training sampleacquired based on the first MRI sequence. The utilization of thereference image in the second training process may facilitate thereconstruction of the predicted full MRI image of the second trainingsample, and improve the training efficiency (e.g., by accelerating modelconvergence) and/or the training accuracy.

In some embodiments, the processing device 120A may generate asubsampling model and an MRI reconstruction model for each of more thantwo MRI sequences. For illustration purposes, FIG. 13 illustrates anexemplary process for generating a subsampling model and an MRIreconstruction model for each of a first, a second, and a third MRIsequence according to some embodiments of the present disclosure. Asshown in FIG. 13 , the first MRI sequence may be a T2-weighted fast spinecho sequence, the second MRI sequence may be a T2-weightedfluid-attenuation inversion recovery sequence, and the third MRIsequence may be T1-weighted fluid-attenuation inversion recoverysequence. It should be noted that the first, second, and third MRIsequences provided in FIG. 13 are merely an example, and this is notintended to be limiting. Subsampling models and MRI reconstructionmodels corresponding to the three MRI sequences may be generated byperforming process 1300 as shown in FIG. 13 .

In 1310, the processing device 120A (e.g., the model generation module420) may generate a first subsampling model and a first MRIreconstruction model corresponding to the first MRI sequence.

Operation 1310 may be performed in a similar manner as operation 1110 asdescribed in connection with FIG. 11 , and the descriptions thereof arenot repeated here.

In 1320, the processing device 120A (e.g., the model generation module420) may generate, based on the first subsampling model and the firstMRI reconstruction model, a second subsampling model and a second MRIreconstruction model corresponding to the second MRI sequence.

In some embodiments, operation 1320 may be achieved by performingoperations 1120 to 1150 as described in connection with FIG. 11 .

In 1330, the processing device 120A (e.g., the model generation module420) may generate a third subsampling model and a third MRIreconstruction model corresponding to the third MRI sequence based onthe first subsampling model, the first MRI reconstruction model, thesecond subsampling model, and the second MRI reconstruction model.

For example, the processing device 120A may obtain at least one thirdtraining sample each of which includes third full MRI data of a thirdtraining subject acquired based on the third MRI sequence. For each ofthe at least one third training sample, the processing device 120A maygenerate a first reference image corresponding to the first MRI sequenceand a second reference image corresponding to the second MRI sequence.The first reference image may be generated based on the firstsubsampling model and the first MRI reconstruction model, for example,in a similar manner as how the reference image of a second trainingsample is generated as described in connection with operation 1130. Thesecond reference image may be generated based on the second subsamplingmodel and the second MRI reconstruction model, for example, in a similarmanner as how the reference image of a second training sample isgenerated as described in connection with operation 1130.

The processing device 120A may further generate the third subsamplingmodel and the third MRI reconstruction model jointly training a thirdpreliminary subsampling model and a third preliminary MRI reconstructionmodel using the at least one third training sample, the at least onefirst reference image, and the at least one second reference image. Insome embodiments, the training process of the third preliminarysubsampling model and the third preliminary MRI reconstruction model(also referred to as a third training process for brevity) may beperformed in a similar manner as the second training process of thesecond preliminary subsampling model and the second preliminary MRIreconstruction model as described in connection with FIG. 12 , exceptthat in each iteration in the third training process, both the firstreference image and the second reference image may be used to generate apredicted full MRI image of a third training sample.

It should be noted that the above descriptions regarding the process1100, the process 1200, and the process 1300 are merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, one ormore operations described above may be omitted and/or one or moreadditional operations may be added. As another example, the intermediateMRI reconstruction model r_(i) in each iteration (e.g., the firstiteration, an iteration subsequent to the first iteration, etc.) of thesecond training process may be the second preliminary MRI reconstructionmodel.

In some embodiments, the processing device 120A may generate the secondsubsampling model and the second MRI reconstruction model based on theat least one second training sample without utilizing the firstsubsampling model and the first MRI reconstruction model. For example,in the process 1100, operations 1110 and 1130 may be omitted, andoperation 1150 may be performed in a similar manner as operation 530 asdescribed in connection with FIG. 5 . Similarly, the processing device120A may generate the third subsampling model and the third MRIreconstruction model based on the at least one third training samplewithout utilizing the first subsampling model, the first MRIreconstruction model, the second subsampling model, and the second MRIreconstruction model.

FIG. 14 is a schematic diagram illustrating an exemplary process forgenerating a target full MRI image of a target subject according to someembodiments of the present disclosure. In some embodiments, process 1400may be executed by the MRI system 100. For example, the process 1400 maybe implemented as a set of instructions (e.g., an application) stored ina storage device (e.g., the storage device 130, the storage device 220,and/or the storage 390). In some embodiments, the processing device 120B(e.g., the processor 210 of the computing device 200, the CPU 340 of themobile device 300, and/or one or more modules illustrated in FIG. 4A)may execute the set of instructions and may accordingly be directed toperform the process 1400.

In 1410, the processing device 120B (e.g., the obtaining module 430) mayobtain target subsampled k-space data of a target subject by performingan MRI scan on the target subject according to a subsampling model.

As used herein, the target subject may include a biological subjectand/or a non-biological subject to be imaged, such as a patient or aspecific portion (e.g., an organ or a tissue) of the patient. Thesubsampling model may define how to perform subsampling during an MRIscan of a subject. During the MRI scan of the target subject, k-spacedata may be subsampled according to the subsampling model, and thesubsampled k-space data may be designated as the target subsampledk-space data of the target subject.

In 1420, the processing device 120B (e.g., the image generation module440) may generate a target subsampled MRI image of the target subjectbased on the target subsampled k-space data.

For example, the target subsampled MRI image may be generated based onthe target subsampled k-space data by, for example, performing aninverse Fourier transformation on the target subsampled k-space data.

In 1430, the processing device 120B (e.g., the image generation module440) may generate a target full MRI image of the target subject byprocessing the target subsampled MRI image using an MRI reconstructionmodel.

The MRI reconstruction model refers to an algorithm or a model (e.g., amachine-learning model) for MRI reconstruction. In some embodiments, thesubsampling model and the MRI reconstruction model may be jointlygenerated by performing a process for generating a subsampling modelcorresponding to an MRI reconstruction model disclosed herein (e.g., theprocess 500 as described in connection with FIG. 5 ). Alternatively, thesubsampling model and/or the MRI reconstruction model may be previouslygenerated by a computing device (the processing device 120A or anotherprocessing device), and stored in a storage device (e.g., the storagedevice 130, a storage device of the terminals(s) 140, or an externalstorage device). The processing device 120B may retrieve the subsamplingmodel and/or the MRI reconstruction model from the storage device.

In some embodiments, the processing device 120B may input the targetsubsampled MRI image into the MRI reconstruction model, or preprocessthe target subsampled MRI image and input the preprocessed targetsubsampled MRI image into the MRI reconstruction model. The MRIreconstruction model may process its input and generate an output. Theoutput of the MRI reconstruction model may include the target full MRIimage or need to be post-processed to generate the target full MRIimage.

In some embodiments, the MRI scan of the target subject may beimplemented by one or more MRI sequences. Each of the one or more MRIsequences may correspond to a specific subsampling model and a specificMRI reconstruction model. The process 1400 may be performed for each MRIsequence to generate a target full MRI image corresponding to the MRIsequence. Merely by way of example, the target subject may be scanned bythe first, second, and third MRI sequences sequentially as shown in FIG.13 . For the first MRI sequence, the processing device 120B may obtain afirst set of target subsampled k-space data of the target subjectaccording to the first subsampling model corresponding to the first MRIsequence. Then, the processing device 120B may generate a first targetsubsampled MRI image of the target subject based on the first set oftarget subsampled k-space data. The processing device 120B may furthergenerate a first target full MRI image of the target subject byprocessing the first target subsampled MRI image using the first MRIreconstruction model corresponding to the first MRI sequence. Similarly,a second target full MRI image of the target subject may be generatedbased on the second subsampling model and the second MRI reconstructionmodel corresponding to the second MRI sequence; and a third target fullMRI image of the target subject may be generated based on the thirdsubsampling model and the third MRI reconstruction model correspondingto the third MRI sequence. By using a specific subsampling model and aspecific MRI reconstruction model for each MRI sequence, the subsamplingefficiency and/or accuracy may be improved, and the accuracy of theresulting target full MRI image(s) may be improved. In some embodiments,the first target full MRI image of the target subject may serve as afirst target reference image, which may be used in the generation of thesecond target full MRI image of the target subject. The second targetfull MRI image of the target subject may serve and a second targetreference image, and the first and second target reference images may beused in the generation of the third target full MRI image of the targetsubject.

It should be noted that the above description regarding the process 1400is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, one or more operations may be added oromitted. For example, operation 1410 and operation 1420 may be combinedinto a single operation. As another example, one or more other optionaloperations (e.g., a storing operation for storing a processing result oran intermediate processing result) may be added in the process 1400.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

1. A system for Magnetic Resonance Imaging (MRI), comprising: at leastone storage device including a set of instructions; and at least oneprocessor configured to communicate with the at least one storagedevice, wherein when executing the set of instructions, the at least oneprocessor is configured to direct the system to perform operationsincluding: obtaining at least one training sample each of which includesfull MRI data; obtaining a preliminary subsampling model and apreliminary MRI reconstruction model; and generating a subsampling modelcorresponding to an MRI reconstruction model by jointly training thepreliminary subsampling model and the preliminary MRI reconstructionmodel using the at least one training sample, the subsampling modelbeing the trained preliminary subsampling model, and the MRIreconstruction model being at least a portion of the trained preliminaryMRI reconstruction model.
 2. The system of claim 1, wherein the jointlytraining the preliminary subsampling model and the preliminary MRIreconstruction model includes an iterative operation including one ormore iterations, and at least one iteration of the one or moreiterations includes: obtaining, based on the preliminary subsamplingmodel and the preliminary MRI reconstruction model, an intermediatesubsampling model and an intermediate MRI reconstruction model;determining whether the intermediate subsampling model satisfies atermination condition; and in response to determining that theintermediate subsampling model does not satisfy the terminationcondition, updating, based on the intermediate MRI reconstruction modeland at least a portion of the at least one training sample, theintermediate subsampling model.
 3. The system of claim 2, wherein the atleast one iteration is the first iteration among the one or moreiterations, the intermediate subsampling model is the preliminarysubsampling model and the intermediate MRI reconstruction model is thepreliminary MRI reconstruction model.
 4. The system of claim 2, whereinthe one or more iterations include a plurality of iterations, the atleast one iteration is subsequent to the first iteration among the oneor more iterations, the intermediate subsampling model is an updatedintermediate subsampling model generated in a previous iteration, andthe intermediate MRI reconstruction model is an updated intermediatesubsampling model generated in the previous iteration or the preliminaryMRI reconstruction model.
 5. The system of claim 2, wherein for the atleast one iteration, the determining whether the intermediatesubsampling model satisfies a termination condition comprises: for eachof the at least a portion of the at least one training sample,generating, based on the intermediate subsampling model and the full MRIdata of the training sample, a subsampled MRI image; generating, basedon the subsampled MRI image and the intermediate MRI reconstructionmodel, a predicted full MRI image; and generating a determination resultof whether the predicted full MRI image satisfies a preset condition;and determining, based on the determination result of each of the atleast a portion of the at least one training sample, whether theintermediate subsampling model satisfies the termination condition. 6.The system of claim 5, wherein for each of the at least a portion of theat least one training sample, the generating a determination result ofwhether the predicted full MRI image satisfies a preset conditioncomprises: obtaining, based on the full MRI data of the training sample,a full MRI image; determining a difference between the full MRI imageand the predicted full MRI image of the training sample; and determiningwhether the predicted full MRI image satisfies the preset condition bydetermining whether the difference exceeds a threshold difference. 7.The system of claim 5, wherein in response to determining that theintermediate subsampling model does not satisfy the terminationcondition, the updating the intermediate subsampling model based on theintermediate MRI reconstruction model and at least a portion of the atleast one training sample comprises: for each of the at least a portionof the at least one training sample, generating, based on the predictedfull MRI image of the training sample, predicted full k-space data;obtaining, based on the full MRI data of the training sample, fullk-space data; and generating a comparison result between the predictedfull k-space data and the full k-space data of the training sample; andupdating the intermediate subsampling model based on the comparisonresult of each of the at least a portion of the at least one trainingsample.
 8. The system of claim 7, wherein the intermediate subsamplingmodel defines a plurality of first k-space lines among a plurality ofk-space lines, for each of the at least a portion of the at least onetraining sample, the full k-space data comprises first data of each ofthe plurality of k-space lines, and the predicted full k-space datacomprises second data of each of the plurality of k-space lines, and thegenerating a comparison result between the predicted full k-space dataand the full k-space data of the training sample comprises: determiningone or more second k-space lines by removing the plurality of firstk-space lines from the plurality of k-space lines; and for each of theone or more second k-space lines, determining a difference between thefirst data of the second k-space line and the second data of the secondk-space line, the comparison result comprising the differencecorresponding to the each of the one or more second k-space lines. 9.The system of claim 2, wherein in response to determining that theintermediate subsampling model does not satisfy the terminationcondition, the at least one iteration of the one or more iterationincludes: updating, based on the at least a portion of the at least onetraining sample, the intermediate MRI reconstruction model.
 10. Thesystem of claim 2, wherein the intermediate MRI reconstruction model isan updated intermediate subsampling model generated in a previousiteration, in response to determining that the intermediate subsamplingmodel satisfies the termination condition, the at least one iteration ofthe one or more iterations includes: designating the intermediatesubsampling model as the subsampling model; and designating theintermediate MRI reconstruction model as the MRI reconstruction model.11. The system of claim 2, wherein the intermediate MRI reconstructionmodel is the preliminary MRI reconstruction model, and in response todetermining that the intermediate subsampling model satisfies thetermination condition, the at least one iteration of the one or moreiterations further includes: designating the intermediate subsamplingmodel as the subsampling model; and generating the MRI reconstructionmodel by updating the intermediate MRI reconstruction model based on theat least a portion of the at least one training sample.
 12. The systemof claim 1, wherein the full MRI data of each of the at least onetraining sample is acquired based on a first MRI sequence, thesubsampling model corresponds to the first MRI sequence, and the atleast one processor is further configured to direct the system toperform the operations including: obtaining at least one second trainingsample each of which includes second full MRI data acquired based on asecond MRI sequence; for each of the at least one second trainingsample, generating a reference image corresponding to the first MRIsequence based on the subsampling model and the MRI reconstructionmodel; obtaining a second preliminary subsampling model and a secondpreliminary MRI reconstruction model; and generating a secondsubsampling model and a second MRI reconstruction model corresponding tothe second MRI sequence by jointly training the second preliminarysubsampling model and the second preliminary MRI reconstruction modelusing the at least one second training sample and the at least onereference image, the second subsampling model being the trained secondpreliminary subsampling model, and the second MRI reconstruction modelbeing the trained second preliminary MRI reconstruction model.
 13. Thesystem of claim 12, wherein for each of the at least one second trainingsample, the generating a reference image corresponding to the first MRIsequence based on the subsampling model and the MRI reconstruction modelcomprises: generating, based on the subsampling model and the secondfull MRI data of the second training sample, a subsampled MRI image; andgenerating the reference image by processing the subsampled MRI imageusing the MRI reconstruction model.
 14. The system of claim 1, whereinthe at least one processor is further configured to direct the system toperform the operations including: obtaining target subsampled k-spacedata of a subject by performing an MRI scan on the subject according tothe subsampling model; generating a target subsampled MRI image of thesubject based on the target subsampled k-space data; and generating atarget full MRI image of the subject by processing the target subsampledMRI image using the MRI reconstruction model.
 15. The system of claim 1,wherein the MRI reconstruction model includes at least one of aconvolution network or a generative adversarial network (GAN).
 16. Asystem for Magnetic Resonance Imaging (MRI), comprising: at least onestorage device including a set of instructions; and at least oneprocessor configured to communicate with the at least one storagedevice, wherein when executing the set of instructions, the at least oneprocessor is configured to direct the system to perform operationsincluding: obtaining target subsampled k-space data of a subject byperforming an MRI scan on the subject according to a subsampling modelcorresponding to an MRI reconstruction model; generating a targetsubsampled MRI image of the subject based on the target subsampledk-space data; and generating a target full MRI image of the subject byprocessing the target subsampled MRI image using the MRI reconstructionmodel, wherein the subsampling model and the MRI reconstruction modelare jointly trained using at least one training sample.
 17. The systemof claim 16, wherein the subsampling model and the MRI reconstructionmodel are jointly trained according to a model training processincluding: obtaining the at least one training sample each of whichincludes full MRI data; obtaining a preliminary subsampling model and apreliminary MRI reconstruction model; and generating the subsamplingmodel corresponding to the MRI reconstruction model by jointly trainingthe preliminary subsampling model and the preliminary MRI reconstructionmodel using the at least one training sample, the subsampling modelbeing the trained preliminary subsampling model, and the MRIreconstruction model being at least a portion of the trained preliminaryMRI reconstruction model.
 18. A method for Magnetic Resonance Imaging(MRI), the method being implemented on a computing device including atleast one processor and at least one storage device, comprising:obtaining at least one training sample each of which includes full MRIdata; obtaining a preliminary subsampling model and a preliminary MRIreconstruction model; and generating a subsampling model correspondingto an MRI reconstruction model by jointly training the preliminarysubsampling model and the preliminary MRI reconstruction model using theat least one training sample, the subsampling model being the trainedpreliminary subsampling model, and the MRI reconstruction model being atleast a portion of the trained preliminary MRI reconstruction model. 19.The method of claim 18, wherein the jointly training the preliminarysubsampling model and the preliminary MRI reconstruction model includesan iterative operation including one or more iterations, and at leastone iteration of the one or more iterations includes: obtaining, basedon the preliminary subsampling model and the preliminary MRIreconstruction model, an intermediate subsampling model and anintermediate MRI reconstruction model; determining whether theintermediate subsampling model satisfies a termination condition; and inresponse to determining that the intermediate subsampling model does notsatisfy the termination condition, updating, based on the intermediateMRI reconstruction model and at least a portion of the at least onetraining sample, the intermediate subsampling model. 20-28. (canceled)29. The method of claim 18, wherein the full MRI data of each of the atleast one training sample is acquired based on a first MRI sequence, thesubsampling model corresponds to the first MRI sequence, and the methodfurther includes: obtaining at least one second training sample each ofwhich includes second full MRI data acquired based on a second MRIsequence; for each of the at least one second training sample,generating a reference image corresponding to the first MRI sequencebased on the subsampling model and the MRI reconstruction model;obtaining a second preliminary subsampling model and a secondpreliminary MRI reconstruction model; and generating a secondsubsampling model and a second MRI reconstruction model corresponding tothe second MRI sequence by jointly training the second preliminarysubsampling model and the second preliminary MRI reconstruction modelusing the at least one second training sample and the at least onereference image, the second subsampling model being the trained secondpreliminary subsampling model, and the second MRI reconstruction modelbeing the trained second preliminary MRI reconstruction model. 30-36.(canceled)